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Based on the MODIS NDVI from 2001 to 2015 and slope data, the surface classification of Chongqing was extracted which is divided into forest land, grassland, cultivated land, mixed land (agriculture, forestry and grass), construction land. There include two key fire prevention areas, one is forest land, grassland, the other is forest land, grassland, mixing land(woodland, grassland, agriculture).The method of distinguishing non-fire hot points was established based on the surface classification and historical RS hot points. Superimpose the hot points with the surface classification, and remove the points fall outside the two key fire zones, extract the points falling in key fire zones. The results showed hot points in the area of key fire prevention was significantly less than that original monitoring points. 5.1% and 15.88% hot points by MODIS fell in the first and second key fire prevention area respectively. 58%, and 68.1% hot points by FY3 fell in two fire prevention area respectively. 53.7% and 73.37% hot points by NOAA fell in fire prevention area respectively. Using surface classification data, hot points in non-forest fire prevention areas can be effectively excluded. Introduction Many methods is used in fire point monitoring by RS data, such as image information enhancement processing methods (space domain and frequency domain), model threshold method, NDVI fire monitoring method and MODIS fire point identification algorithm[1]. Giglio improved the MODIS fire point monitoring algorithm, which strengthened the position of MODIS in forest fire monitoring and identification[2]. Other data have been confirmed in forest fire monitoring, such as title map image, SPOT, Quickbird, NOAA-AVHRR, VIRS-NPP. Aircraft or special monitoring towers also play a considerable role in forest fire monitoring[3-5]. RS can monitor the occurrence of forest fires, analyze forest fire spatial distribution, assess fire risk, simulate and predict fire behavior[6-7]. In recent years, meteorological satellite imagery has also been applied to the assessment of ecological environment loss after forest fires. Hall used RS data to study the dynamic change and post-disaster assessment of fire[8-9], Cuevas[10] study vegetation reconstruction after fire using RS data, SONG [11] used 0.83μm and 1.66μm reflection band to monitor the fire area. Yin Haiwei[12] selected forest fire factors including vegetation types, and quantitatively evaluated the forest fire risk of Yuying Forest Farm and Fendou Forest Farm in Daxing'anling. Xu Hong[13] used RS data and GIS technology to extract the flammability grades and vegetation dryness of different vegetations, and combined the forest meteorological fire risk prediction grade results, using the multi-factor weighted overlay method to calculate the forest fire risk prediction index of Yunnan Province. Huang Baohua[14] based on MODIS data, extracted relative humidity, temperature, vegetation data, 10h time lag combustibles humidity, normalized water index, vegetation greenness and other factors, constructed a Fire Potential Index. Qin Xianlin[15-16] used MODIS data to extract spatial distribution of forest fuel combustibles and water content, and constructed a forest fire warning model based on the fire risk index to predict the forest fire occurrence level of major forest fires. Deng Ou[17] constructed a spatial Logistic forest fire risk prediction model using RS datasets of the fire-burning grounds, and conducted related research on forest fire risk zoning. Wang Yikai[18] obtained a new model for fire risk prediction based on RS data to obtain surface temperature field and other factors.

Discussion on the Method of Removing Abnormal Hot Point in Forest Fire Remote Sensing Monitoring Based on Vegetation Index